This present invention relates generally to the field of financial modeling and more particularly to methods and systems for constructing and implementing such models to manage characteristics of a financial account portfolio, such as a credit card portfolio, and for use in retail banking, brokerage and insurance accounts in an effective manner.
Relating specifically to the bankcard industry, it is well known that annual percentage rate (APR) and available credit line of an account are critical factors influencing card usage and, subsequently, bank profitability. Similarly brokerage fees, retail banking loan rates, and yearly insurance re-pricing influence profitability in those industries. In the credit card industry, lower APRs and higher credit lines are more attractive to consumers. However, APRs that are too low may reduce bank profitability, while indiscriminate line increases may adversely affect risk management such as by dramatically increasing credit loss exposure.
Balance accumulation and customer retention are important goals for bankcard issuers, there are two broad approaches to achieve this objective are: take unilateral action to influence the desired behavior (i.e., increased card usage), and take measures that require initial customer response before the desired behavior results. Measures such as APR credit line and fee adjustments and other pricing changes generally fall in the first category. The second category generally consists of measures such as mailing convenience checks, balance transfer checks and monthly statement checks, each of which requires the customer to make the decision to respond to the offer before the benefits to the bank accrue. Because the unilateral approach lacks a direct response, it may not be readily apparent that a unilateral action results in a desired behavior at all and/or it may prove difficult to detect or measure. For example, though there is anecdotal evidence that line increases may spur increased card usage, there may be countervailing evidence that many account holders simply ignore line increases. While the effect of pricing changes is often perceived to have the greatest impact on driving customer behavior, because most price changes increase the APR, called re-pricing, the resulting effect is often to reduce card usage. Because the unilateral approach may be fully implemented controlled and monitored by the issuing bank, accurate modeling methods in this regard have substantial value.
Although there are some known modeling methods for granting initial credit, significantly fewer solutions exist regarding the management of existing credit lines and pricing. In one known methodology, statistical models are developed using a Bayesian approach and a Markov decision model to make the initial credit granting decision. However, little research has been published that relates to adjusting the base price of card products once issued. Of more immediate relevance to the present invention is the decision to periodically change credit limits and pricing. Credit limit increases for existing cardholders may be used as a tactical marketing tool and are routinely made to increase card usage. Increasing credit limits does, has been found to influence increased spending among certain consumers. However, it has also been found that the more savvy consumers are not as readily affected by credit limit increases. Additionally, it is believed that some customers who pay their bills in full each month may be completely insensitive to the base APR of their product. Alternatively, pricing a card competitively can lead to increased sales and usage by those customers who are price sensitive.
Adding to the issue is the fact that the above understandings have traditionally been studied in conjunction with the effects of credit limit changes for a customer with a single card. Since each modern customer typically has an average of 4.2 cards, the more interesting effects relate to seeing if charges may be shifted from one to another due to a line increase, even if the level of total debt for a cardholder does not change, an effect known as balance migration. Since card issuers typically see only activity on their card, and the various credit bureaus only provide aggregate data over all bankcard balances, it is often difficult to differentiate between new activity and balance migration.
Conventionally, processes exist for making line management decisions, but do not include policies for reducing customer base APRs. One conventional approach for line change decisions that is common in the credit card industry is known as a decision tree analysis. In this approach, a portfolio is segmented by models that predict a customer's future risk, profitability and likelihood of discontinuing card usage, or attrition. Customer credit bureaus and internal card usage information are also commonly used to define various segments. These variables measure payment, sales, bankcard revolving balances, delinquency history, and so on. The models or scores and variables are grouped into intervals. The inverted decision tree starts from a root variable and has as many levels as the variables being used. At each level the tree branches into each interval of that variable, and at the bottom of the tree the leaf nodes specify the amount of credit line increase to be given. An example decision rule may be “If the current credit line is $2000, and balance is $1500, then if the risk score is 650, give a line increase of $1000, for risk scores between 600 and 650, give a line increase of $750, etc.” There are a number of commercial rule engines available to deploy these decision criteria. Examples of such rule engines include products from ILOG (JRules Version 4.0) and Fair Isaac and Co. (TRIAD version 7.0).
In general, existing decision tree methodology for line change or management has been in place for many years. Some variables invalid in the decision tree are credit bureau scores on risk, revenue, etc. Although these proprietary models are considered to be fairly accurate in projecting the future earnings and losses of a customer, they may not be considered sufficient in evaluating customer spending, usage and payment under different pricing and line change scenarios. For example, these models used information on usage and payment over all cards and other debts (auto, mortgage, etc.) that a cardholder has to predict delinquency and future profitability. However, this information alone is not considered sufficient in evaluating usage resulting from additional line amounts or reductions in APR. The known decision tree methodologies are also geared more towards using current account behavior (such as utilization of current line) and is not capable of considering or predicting future account behavior that may result from a change in line or price.
Additionally, at many card issuing institutions, line change decisions are evaluated for each account periodically or every so often, e.g., every few months or more frequently if necessary. In addition, ad hoc line increases may be given in in-store situations where the cardholder bumps against their current limit and would otherwise not be able to make a large ticket purchase. Line increases are also evaluated as results of a specific customer request to inbound call centers.
Limitations associated with prior business practices present important opportunities for improvements in the treatment of customers, operational difficulties and lost savings. First, credit lines and pricing are highly visible to customers and result in a highly competitive business environment. Our goals is to offer a line and/or pricing change consistent with the needs and utility that a customer would derive from such actions while minimizing risk. Shortcomings in stimulating sales and balance growth from a customer base may well affect financial performance. From a risk management perspective, the amount of incremental net credit loss incurred for the amount of line that a bank or card issuer gives its customers is disproportionately large. Data suggests that lines are more than competitive with the marketplace but that dollars charged off relative to outstanding balances are high. Second, pricing changes are conventionally triggered by late payments. A customer's APR may be increased based on failure to make timely payment(s). Often, once the APR is raised, it does not automatically decrease based on subsequent customer behavior. Pricing models are focused on pricing at the time of acquisitions or pricing of convenience checks.
Accordingly, there is a clear need in the area of predictive financial modeling for a method and system for accurately enabling credit line management and price management according to a customer's inherent needs and also incorporating any business rules consistent with the issuing bank's own business constraints regarding risk and return.